Name
Title | ||
|---|---|---|
Deep Reinforcement Learning for Dynamic Reliability Aware NFV-Based Service Provisioning. |
Abstract | ||
|---|---|---|
Network function virtualization (NFV) is referred to the technology in which softwarized network functions virtually run on commodity servers. Such functions are called virtual network functions (VNFs). A specific service is composed of a set of VNFs. This is a paradigm shift for service provisioning in telecom networks which introduces new design and implementation challenges. One of such challenges is to meet the reliability requirement of the requested services considering the reliability of the commodity servers. NFV placement which is the problem of assigning commodity servers to the VNFs becomes crucial under such circumstances. To address such an issue, in this paper, we employ Deep Reinforcement Learning (Deep-RL) to model NFV placement problem considering the reliability requirement of the services. The output of the introduced model determines optimal placement in each state. Numerical evaluations show that the introduced model can significantly improve the performance of the network operator. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/GLOBECOM38437.2019.9013214 | GLOBECOM |
DocType | Citations | PageRank |
Conference | 1 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hamed Rahmani Khezri | 1 | 1 | 0.34 |
| Puria Azadi Moghadam | 2 | 1 | 0.34 |
| Mohammad Karimzadeh-Farshbafan | 3 | 6 | 1.78 |
| V. Shah-Mansouri | 4 | 160 | 24.25 |
| Hamed Kebriaei | 5 | 70 | 14.91 |
| Niyato Dusit | 6 | 9486 | 547.06 |